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X-WR-CALDESC:Events for Biomedical Mathematics Group
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TZID:Asia/Seoul
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TZOFFSETFROM:+0900
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DTSTART:20220101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230203T140000
DTEND;TZID=Asia/Seoul:20230203T160000
DTSTAMP:20260426T044143
CREATED:20230126T234906Z
LAST-MODIFIED:20230130T080459Z
UID:7274-1675432800-1675440000@www.ibs.re.kr
SUMMARY:Hyukpyo Hong\, Estimating and Assessing Differential Equation Models with Time-Course Data
DESCRIPTION:We will discuss about “Estimating and Assessing Differential Equation Models with Time-Course Data”\, Wong\, Samuel WK\, Shihao Yang\, and S. C. Kou\, arXiv preprint arXiv:2212.10653 (2022). \nAbstract \n\nOrdinary differential equation (ODE) models are widely used to describe chemical or biological processes. This article considers the estimation and assessment of such models on the basis of time-course data. Due to experimental limitations\, time-course data are often noisy and some components of the system may not be observed. Furthermore\, the computational demands of numerical integration have hindered the widespread adoption of time-course analysis using ODEs. To address these challenges\, we explore the efficacy of the recently developed MAGI (MAnifold-constrained Gaussian process Inference) method for ODE inference. First\, via a range of examples we show that MAGI is capable of inferring the parameters and system trajectories\, including unobserved components\, with appropriate uncertainty quantification. Second\, we illustrate how MAGI can be used to assess and select different ODE models with time-course data based on MAGI’s efficient computation of model predictions. Overall\, we believe MAGI is a useful method for the analysis of time-course data in the context of ODE models\, which bypasses the need for any numerical integration.
URL:https://www.ibs.re.kr/bimag/event/2023-02-03-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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